Renewable Energy for Sustainable Growth Assessment. Группа авторов

0.0807 0.3369 5 V_j⁺ 0.0611 0.0822 0.0671 0.0379 0.0682 0.0001 0.0001 0.0574 0.0144 0.0135 V_j^- 0.0114 0.0164 0.0189 0.0500 0.0273 0.0978 0.0993 0.0115 0.0722 0.0674

Table 2.5 Normalized decision matrix for fuzzy-TOPSIS.

RE technology	I1	I2	I3	I4	I5	I6	I7	I8	I9	I10
Large hydropower	1.00	1.00	0.59	1.00	1.00	0.82	0.94	0.00	0.00	0.00
Small hydropower	1.00	0.00	0.59	0.13	0.50	1.00	1.00	0.50	0.50	0.50
Solar PV	0.00	0.00	0.00	0.00	0.17	0.98	0.81	1.00	1.00	0.75
Onshore wind power	0.31	0.00	0.15	0.80	0.17	0.89	0.97	1.00	0.75	1.00
Bioenergy	0.54	0.50	1.00	0.33	0.00	0.00	0.00	0.75	0.50	0.25
Wj	0.10	0.10	0.10	0.10	0.10	0.10	0.10	0.10	0.10	0.10

Figure 2.2 Fuzzy triangular membership function [88].

2.5.3 Monte Carlo Simulations–Based Probabilistic Ranking

The uncertainties related to a wide range in input values have been addressed by the TOPSIS method run using Monte Carlo simulation (MCS). For MCS each indicator value (Table 2.3) was randomly sampled with uniform distribution for 10,000 simulations. These randomly sampled variables are used as input to the TOPSIS method and probabilistic ranking was obtained. The histograms obtained of the ranking for each of RE technologies from the 10,000 MCS are presented in Figure 2.3. It can be seen from the histogram that small hydropower is on the top rank in more than 80% of simulations and bioenergy is on the bottom rank in more than 90% of simulations. While large hydropower has distributed ranking (from 1 to 4) in the range of 10% to 45% number of simulation cases.

Table 2.6 Decision matrix with fuzzy linguistic variable.

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RE technology	I1	I2	I3	I4	I5	I6	I7	I8	I9	I10
Large hydropower	Very high	Very high	Medium	Very high	Very high	High	Very high	Very low	Very low	Very low
Small hydropower	Very high